Creating visualizations of large data sets stored in a database is a difficult problem. Resolution and pixel count limit the amount of data points that can be displayed on any visual display. While modern mobile devices, for example smartphones and tablet computers, have the capability to provide a rich and diverse set of data to an end user, smaller displays and a reduced pixel count further exacerbate the problem. Limited visual acuity of the human eye can reduce massive numbers of data point plotting symbols on a visual display into an uninterpretable collection of data points. In addition, available network bandwidth, for example bandwidth associated with a cellular network, can act as a bottleneck when attempting to transfer a large data set to a mobile device for visualization. Various methods of what is called “extreme visualization” can be used to attempt to visualize the large data sets as various types of graphs. For example, a common method is to aggregate many tuples, or sets of data elements, into separate bins, each bin representing a data point that is the average of a certain number of data points in the large data set. The bins are then visualized on a display as a graph to represent the data. Other methods include kernel density estimation and cumulative distribution functions. The classic approach to performing this type of data aggregation is using non-real time, complex, data-, processing-, and schema-intensive database queries, for example queries in structured query language (SQL), to process the data for display.